The basis of neural networks is the model formula (Formula 1) proposed by Frank Rosenblatt in 1958 that shows the relationship between input signals xi and an output signal y of a neuron. In Formula 1, Wi is the weight; θ is the threshold; and f(x) is the Heaviside step function. Also, f(x) is shown in Formula 2.
                    y        =                  f          ⁡                      (                                                            ∑                                      i                    =                    1                                    n                                ⁢                                                      W                    i                                    ⁢                                      x                    i                                                              -              θ                        )                                              [                  Formula          ⁢                                          ⁢          1                ]                                          f          ⁡                      (            x            )                          =                  {                                                    0                                                              (                                      x                    ≤                    0                                    )                                                                                    1                                                              (                                      x                    >                    0                                    )                                                                                        [                  Formula          ⁢                                          ⁢          2                ]            
When making a learning type neural network based on the model formulas, generally, a weight learning type model is used in which each of the weights Wi are used as the effect variables of the learning.